Interactive Dialogue System for Context-Aware Recommendation Reasoning
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Solution Overview
Problem
On-device, target-oriented dialogue systems generate static search recommendations based on historical user data, often resulting in inaccurate results and failing to determine why users like or dislike items, making it difficult for users to explore temporary interests or make specific requests.
Innovation Solution
A multi-reward interactive dialogue system using reinforcement learning that performs target-oriented question-answering based on user dialogue context, providing reasoning summaries and justifications, and dynamically deciding between providing recommendations or asking follow-up questions based on similarity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If on-device dialogue systems use historical user data to generate search recommendations, then the system can provide personalized recommendations, but the recommendations become static and inaccurate rather than interactive and context-based
Solution Approach 1:
The patent transforms static recommendation generation into a dynamic interactive dialogue system that adapts in real-time to user responses. The system maintains a dialogue state tracker that continuously updates based on user feedback, enabling recommendations to evolve dynamically rather than remaining static based solely on historical data.
Solution Approach 2:
The system implements explicit feedback mechanisms where user responses to recommendations are captured and processed to refine subsequent recommendations. The dialogue state tracker incorporates user feedback loops, allowing the system to learn from interactions and improve recommendation accuracy through continuous feedback rather than relying only on pre-collected historical data.
2Ease of operation
If dialogue systems rely on simple user feedback like ratings or clicks, then the system can process feedback easily, but the system cannot determine why users like or dislike items
Solution Approach 1:
The patent introduces a dialogue state tracker as an intermediary component that bridges simple user feedback and complex preference reasoning. This tracker maintains structured representations of user intentions, constraints, and preferences throughout the dialogue, transforming raw feedback into actionable insights without requiring users to provide explicit reasoning.
Solution Approach 2:
The system adds a temporal dimension to feedback processing by maintaining dialogue history and evolving user states across multiple interaction turns. This transforms static single-point feedback into a dynamic trajectory of user preferences, enabling the system to infer reasoning behind preferences through the evolution of dialogue states rather than analyzing isolated feedback points.
3Speed
If the system provides static search recommendations, then the system can respond quickly to user requests, but users cannot explore temporary interests or make specific requests effectively
Solution Approach 1:
The system performs preliminary actions by proactively asking clarifying questions and gathering user constraints before generating recommendations. The dialogue state tracker pre-processes user inputs to identify implicit needs and preferences, enabling the system to prepare more accurate recommendations in advance rather than responding generically to initial queries.
Solution Approach 2:
The system employs dynamic recommendation generation that adapts to user responses in real-time. Rather than providing static recommendations based on initial queries, the system evolves its understanding of user needs through dialogue turns, allowing users to explore temporary interests by providing feedback that dynamically reshapes the recommendation space.
Data Source
AI summary
A method includes receiving natural-language input from a user. The method also includes receiving, from an information source, one or more candidate recommendations as potential responses to the natural-language input. The method further includes determining, based on a similarity between the natural-language input and a selected candidate recommendation among the one or more candidate recommendations, whether to respond to the natural-language input with natural-language output that includes (i) the selected candidate recommendation or (ii) a query for additional user input. In addition, the method includes providing, based on the determination, the natural-language output to the user.